🌮 Data Analyst Vs Data Engineer Vs Data Scientist 🌮


Skills required to become data analyst
👉 Advanced Excel, Oracle/SQL
👉 Python/R

Skills required to become data engineer
👉 Python/ Java.
👉 SQL, NoSQL technologies like Cassandra or MongoDB
👉 Big data technologies like Hadoop, Hive/ Pig/ Spark

Skills required to become data Scientist
👉 In-depth knowledge of tools like R/ Python/ SAS.
👉 Well versed in various machine learning algorithms like scikit-learn, karas and tensorflow
👉 SQL and NoSQL

Bonus skill required: Data Visualization (PowerBI/ Tableau) & Statistics

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Here are 5 key Python libraries/ concepts that are particularly important for data analysts:

1. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data. Pandas offers functions for reading and writing data, cleaning and transforming data, and performing data analysis tasks like filtering, grouping, and aggregating.

2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is often used in conjunction with Pandas for numerical computations and data manipulation.

3. Matplotlib and Seaborn: Matplotlib is a popular plotting library in Python that allows you to create a wide variety of static, interactive, and animated visualizations. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive and informative statistical graphics. These libraries are essential for data visualization in data analysis projects.

4. Scikit-learn: Scikit-learn is a machine learning library in Python that provides simple and efficient tools for data mining and data analysis tasks. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. Scikit-learn also offers tools for model evaluation, hyperparameter tuning, and model selection.

5. Data Cleaning and Preprocessing: Data cleaning and preprocessing are crucial steps in any data analysis project. Python offers libraries like Pandas and NumPy for handling missing values, removing duplicates, standardizing data types, scaling numerical features, encoding categorical variables, and more. Understanding how to clean and preprocess data effectively is essential for accurate analysis and modeling.

By mastering these Python concepts and libraries, data analysts can efficiently manipulate and analyze data, create insightful visualizations, apply machine learning techniques, and derive valuable insights from their datasets.

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🔟 Project Ideas for a data analyst

Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.

Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.

Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.

Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.

Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.

Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.

Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.

A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.

Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.

Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.

Remember to choose a project that aligns with your interests and the domain you're passionate about.

Data Analyst Roadmap
👇👇
https://news.1rj.ru/str/sqlspecialist/379

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Skills need for everyday data analysis jobs

MUST ADD these 5 POWER Bl projects to your resume to get hired

Here are 5 mini projects that not only help you to gain experience but also it will help you to build your resume stronger

📌Customer Churn Analysis
🔗 https://www.kaggle.com/code/fabiendaniel/customer-segmentation/input

📌Credit Card Fraud
🔗 https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

📌Movie Sales Analysis
🔗https://www.kaggle.com/datasets/PromptCloudHQ/imdb-data

📌Airline Sector
🔗https://www.kaggle.com/datasets/yuanyuwendymu/airline-

📌Financial Data Analysis
🔗https://www.kaggle.com/datasets/qks1%7Cver/financial-data-

Simple guide

1. Data Utilization:
- Initiate the process by using the provided datasets for a comprehensive analysis.

2. Domain Research:
- Conduct thorough research within the domain to identify crucial metrics and KPIs for analysis.

3. Dashboard Blueprint:
- Outline the structure and aesthetics of your dashboard, drawing inspiration from existing online dashboards for enhanced design and functionality.

4. Data Handling:
- Import data meticulously, ensuring accuracy. Proceed with cleaning, modeling, and the creation of essential measures and calculations.

5. Question Formulation:
- Brainstorm a list of insightful questions your dashboard aims to answer, covering trends, comparisons, aggregations, and correlations within the data.

6. Platform Integration:
- Utilize Novypro.com as the hosting platform for your dashboard, ensuring seamless integration and accessibility.

7. LinkedIn Visibility:
- Share your dashboard on LinkedIn with a concise post providing context. Include a link to your Novypro-hosted dashboard to foster engagement and professional connections.

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Hope this helps you :)

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝗶𝗻 𝗮 𝗿𝗲𝗮𝗹 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄?

𝗕𝗮𝘀𝗶𝗰 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻

-Brief introduction about yourself.

-Explanation of how you developed an interest in learning Power BI despite having a chemical background.


𝗧𝗼𝗼𝗹𝘀 𝗣𝗿𝗼𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆

-Discussion about the tools you are proficient in.

-Detailed explanation of a project that demonstrated your proficiency in these tools.

𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗘𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻

Explain about any Data Analytics Project you did, below are some follow-up questions for sales related data analysis project

Follow-up Question:

Was there any improvement in sales after building the report?

Provide a clear before and after scenario in sales post-report creation.

What areas did you identify where the company was losing sales, and what were your recommendations?

- How do you check the quality of data when it's given to you?
Explain your methods for ensuring data quality.

- How do you handle null values? Describe your approach to managing null values in datasets.


𝗦𝗤𝗟 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀

-Explain the order in which SQL clauses are executed.

-Write a query to find the percentage of the 18-year-old population.
Details: You are given two tables:
Table 1: Contains states and their respective populations.
Table 2: Contains three columns (state, gender, and population of 18-year-olds).

-Explain window functions and how to rank values in SQL.

- Difference between JOIN and UNION.

-How to return unique values in SQL.

𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀

-Solve a puzzle involving 3 gallons of water in one jar and 2 gallons in another to get exactly 4 gallons.
Step-by-step solution for the water puzzle.

- What skills have you learned on your own? Discuss the skills you self-taught and their impact on your career.

-Describe cases when you showcased team spirit.

-⭐ 𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮 𝗔𝗽𝗽 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻
Scenario: Choose any social media app (I choose Discord).
Question: What function/feature would you add to the Discord app, and how would you track its success?

- Rate yourself on Excel, SQL, and Python out of 10.

- What are your strengths in data analytics?

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If you want to be a data analyst, you should work to become as good at SQL as possible.

1. SELECT

What a surprise! I need to choose what data I want to return.

2. FROM

Again, no shock here. I gotta choose what table I am pulling my data from.

3. WHERE

This is also pretty basic, but I almost always filter the data to whatever range I need and filter the data to whatever condition I’m looking for.

4. JOIN

This may surprise you that the next one isn’t one of the other core SQL clauses, but at least for my work, I utilize some kind of join in almost every query I write.

5. Calculations

This isn’t necessarily a function of SQL, but I write a lot of calculations in my queries. Common examples include finding the time between two dates and multiplying and dividing values to get what I need.

Add operators and a couple data cleaning functions and that’s 80%+ of the SQL I write on the job.